rowMeans Function
https://www.programmingr.com/tutorial/rowmeans-in-r/
library(data.table)
library(plotly)
likeability <- fread("likeability_formatted.csv")
# JITAI 1
# Numeric
jitai_1_numeric <- likeability[ which(likeability$JITAI_TYPE=='1' & likeability$NUMERIC_TYPE!='Numeric'), 4:8]
jitai_1_numeric_means <- rowMeans(jitai_1_numeric)
mean_jitai_1_numeric <- mean(jitai_1_numeric_means)
# Non-Numeric
jitai_1_non_numeric <- likeability[ which(likeability$JITAI_TYPE=='1' & likeability$NUMERIC_TYPE!='Non-numeric'), 4:8]
jitai_1_non_numeric_means <- rowMeans(jitai_1_non_numeric)
mean_jitai_1_non_numeric <- mean(jitai_1_non_numeric_means)
# JITAI 2
# Numeric
jitai_2_numeric <- likeability[ which(likeability$JITAI_TYPE=='2' & likeability$NUMERIC_TYPE!='Numeric'), 4:8]
jitai_2_numeric_means <- rowMeans(jitai_2_numeric)
mean_jitai_2_numeric <- mean(jitai_2_numeric_means)
# Non numeric
jitai_2_non_numeric <- likeability[ which(likeability$JITAI_TYPE=='2' & likeability$NUMERIC_TYPE!='Non-numeric'), 4:8]
jitai_2_non_numeric_means <- rowMeans(jitai_2_non_numeric)
mean_jitai_2_non_numeric <- mean(jitai_2_non_numeric_means)
# Numeric v. Non Numeric
# Numeric
jitai_numeric <- likeability[ which( likeability$NUMERIC_TYPE!='Numeric'), 4:8]
jitai_numeric_means <- rowMeans(jitai_numeric)
mean_jitai_numeric <- mean(jitai_numeric_means)
# Non Numeric
jitai_non_numeric <- likeability[ which( likeability$NUMERIC_TYPE!='Non-numeric'), 4:8]
jitai_non_numeric_means <- rowMeans(jitai_non_numeric)
mean_jitai_non_numeric <- mean(jitai_non_numeric_means)
# Plot
# https://plotly.com/r/bar-charts/
if (TRUE) {
MessageTypes <- c("JITAI 1", "JITAI 2")
Numeric <- c(mean_jitai_1_numeric, mean_jitai_2_numeric)
Non_Numeric <- c(mean_jitai_1_non_numeric, mean_jitai_2_non_numeric)
data <- data.frame(Message_Types, Numeric, Non_Numeric)
fig <- plot_ly(
data,
x = ~Message_Types,
y = ~Numeric,
type = 'bar',
name = 'Numeric',
text = ~Numeric,
textposition = 'auto'
)
fig <- fig %>% add_trace(
y = ~Non_Numeric,
name = 'Non Numeric',
text = ~Non_Numeric,
textposition = 'auto'
)
fig <- fig %>% layout(
title = "Numeric v. Non-Numeric",
yaxis = list(title = 'Likeability',
range = c(0, 5)),
barmode = 'group'
)
fig
}
table <- data.frame(x = c("Numeric", "Non Numeric"),
y = c(mean_jitai_numeric, mean_jitai_non_numeric))
table$x <- factor(table$x, levels = c(as.character(table$x)))
fig <- plot_ly(
data=table,
x = ~x,
y = ~y,
name = "JITAI",
type = "bar",
text = ~y,
textposition = 'auto'
)
fig <- fig %>% layout(title = "Numeric v. Non-Numeric", xaxis = list( title = "Numeric Type"), yaxis = list( title = "Likeability", range = c(0, 5)))
fig
NA
Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Cmd+Option+I.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Cmd+Shift+K to preview the HTML file).
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
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